Wireless network service assessment

ABSTRACT

Methods of creating an indoor confidence level comprising: receiving a location and location accuracy value from or for a device, wherein the location accuracy value is equated to a location accuracy circle; comparing the location and location accuracy circle to a map of known buildings and outdoor locations; and defining an indoor confidence level based upon the percent of overlap of the accuracy radius to a building on said map; and methods of defining an optimization priority among a set of collected data points for network connectivity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application No. 62/761,924 filed on Apr. 11, 2018 with the US Patent and Trademark Office, the contents of which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

Wireless performance and connectivity are modern issues and continue to expand as cellular and wireless connectivity and transmission of data has become ubiquitous. Not all network plans are the same and coverage, performance and connectivity for one provider is inherently different than coverage, performance, and connectivity for another provider based upon the location of towers and signal generators. While generally more coverage and stronger signals are better, efficiently providing coverage would generate improved performance at reduced costs to providers. This in turn can reduce costs to participants or allow for further extension of networks or improvements to other areas of the network. Accordingly, cellular network operators and others continually assess the coverage and performance of wireless networks to identify areas of potential improvement and to identify competitive position and potential opportunities to increase sales of wireless service in an area.

Coverage and performance of cellular networks or mobile networks can be measured using a variety of methods, such as using portable test equipment or gathering measurements directly from the network equipment that is providing wireless service. For example, portable testing equipment can be used to manually gather signal strength and noise measurements by movement along or within an area of interest. This can allow collection of such data generally within an area by persons walking, driving, or otherwise moving within an area. Furthermore, information can be combined with this collected data and measured directly from the network equipment. Together, these data points can assist with assessing performance of the network.

However, traditional methods of assessing network coverage and performance are limited in sample count, volume, and location accuracy. Our techniques improve the ability of interested parties to assess wireless network coverage, performance, and demand in larger areas and higher resolution than previously possible, and to prioritize areas of potential network improvement and sales opportunities.

SUMMARY OF THE INVENTION

The embodiments described herein are directed toward methods and systems for improving network system performance for wireless networks, optimizing the same, determining indoor network performance, and methods for determining optimization priority of a network.

In a preferred embodiment, a method comprises capturing a measurement of network performance from a device application, network performance counter, or call trace that contains a reported horizontal and/or vertical location and one or more error values; defining wherein the one or more error values are a circular area with the radius equal to the location accuracy; using a computer mapping system wherein the reported horizontal and vertical location and error value is defined on a polygonal map defining existing structures and roads; and defining an indoor confidence level based upon the proportion of the area of the error value that falls within a structure as defined on the polygonal map.

In further embodiments, wherein the polygonal map is a 2-dimensional map.

In further embodiments, the polygonal map is a 3-dimensional map.

In further embodiments, the error value is a sphere, having a radius equal to the location accuracy.

In a further embodiment, an additional weight is applied to the indoor confidence level where the device is connected to a WiFi network. In a preferred embodiment, the device's location and location accuracy is measured from crowdsourced data, network performance counters and/or call traces.

In a further embodiment, an additional weight is applied to the indoor confidence level if the device is charging.

In a further embodiment, an additional weight is applied to the indoor confidence level if the device is stationary for a predetermined amount of time. In preferred embodiments, the predetermined amount of time is at least 1 minute, at least 2 minutes, at least 5 minutes, at least 10 minutes, at least 15 minutes, at least 30 minutes, or at least 60 minutes.

In a further embodiment, an additional weight is applied to the indoor confidence level if a reported location change is too far from a previous reported location to have travelled in an elapsed time since the previous reported location.

In a further embodiment, an additional weight is applied to the indoor confidence level if a reported location of a previous measurement at a similar day and time is similar to a subsequent measured location at the said day and time.

In a further embodiment, if the indoor confidence level is calculated above a particular threshold (e.g. 40 or 50%) and wherein the error value overlaps within a pre-defined margin of a structure, then if an indoor confidence level is determined, the measurement is defined as being within said structure.

A further embodiment is directed toward an optimization priority, wherein an optimization score is defined for a given signal by comparing signal level (for example, RSRP (Reference Signal Receive Power)) to signal quality (for example, RS SNR (Reference Signal-to-Noise Ratio) or RSRQ (Reference Signal Receive Quality)). Signal level and signal quality can be detected for systems such as 2G, 3G, 4G, 5G, LTE, or systems of the like now or in development in the future. Additional signal and quality KPIs may also include RSSI, RxLev, EcIo, SS-RSRP, SS-SINR, and others as known to those of ordinary skill in the art. Said KPI's may be interchanged with those referred to herein as understood by those of skill in the art.

In a preferred embodiment, signal level and quality are KPI (key performance indicators) which are typically correlated and can be quantified during the network planning phase, using signal propagation and modeling tools; post-launch optimization phase with RF measurements collection using a dedicated drive-test apparatus and manual processing and analysis of data logs; or Mature Network Optimization Phase, wherein network service measurements collection from a large number of existing customers and automatic processing and visualization are utilized.

In a preferred embodiment, an optimization score is defined by normalizing a deviation of a measurement's signal level and quality from (a) the area average signal level and quality and (b) the ideal signal level and quality achievable; and calculating a score using predefined quality value thresholds to generate an optimization score between 0 and 100.

A further embodiment comprises calculating a score for optimization priority of a signal measurement within a measurement set wherein one of four scenarios exist namely: wherein (1) the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and lower than the actual RS SNR; (2) the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and higher than the actual RS SNR; (3) the slope of RS SNR_([AVERAGE]) is higher than the slope of RS SNR_([IDEAL]) and lower than the actual RS SNR; (4) the slope of RS SNR_([AVERAGE]) is higher than the slope of RS SNR_([IDEAL]) and higher than the actual RS SNR. These scenarios can be optimized by calculating the priority score according to the formulae defined herein, in order to generate an optimization priority for a single measurement within a dataset.

A further embodiment comprises a method of creating an indoor confidence level comprising: receiving a location and location accuracy value from or for a device, wherein the location accuracy value is equated to a location accuracy circle; comparing the location and location accuracy circle to a map of known buildings and outdoor locations; and defining an indoor confidence level based upon the percent of overlap of the accuracy radius to a building on said map.

In a further embodiment of the method of creating an indoor confidence level, wherein a map of known building footprints is an electronically defined map comprising a plurality of polygons, each polygon defining a known building or structure, which are each defined as being indoors, and wherein all other space on said map is defined as outdoors.

In a further embodiment of the method of creating an indoor confidence level, wherein the indoor confidence level defined upon the percent of overlap of the accuracy radius to a building on said map is an initial indoor confidence level.

In a further embodiment of the method of creating an indoor confidence level, wherein the initial indoor confidence level is modified based upon one or more additional steps, selected from the group consisting of: detecting whether a device is connected to a WiFi network; detecting whether the device battery is charging, detecting if the device is stationary at a high confidence location, detecting whether the device moves a significant distance in a period of time “T” that provides indication of unreliable location mapping; detecting a location and comparing to a similar time and location from a previous day; and combinations thereof.

In a further embodiment of the method of creating an indoor confidence level, wherein the process of detecting whether a device is connected to a WiFi network is further defined by comparing the strength of the WiFi signal to a signal threshold variable in order to determine whether a device is connected to a WiFi network.

In a further embodiment of the method of creating an indoor confidence level, wherein the process of detecting whether the device battery is charging detects whether the device is connected to A/C power.

In a further embodiment of the method of creating an indoor confidence level, wherein the process of detecting if the device is stationary at a high confidence location comprises detecting if a device is stationary for a time period “T” and wherein the high confidence location is one that has an indoor confidence level of at least 50, or which has previously been categorized as being indoors.

In a further embodiment of the method of creating an indoor confidence level, wherein the process of detecting whether the device moves a significant distance in a period of time “T” is defined as capturing a set of location datapoints and comparing a set of location datapoints over a time period T₁, and wherein if any data point moves a distance greater than possible during the period of time “T” then that distance is identified as being unreliable.

In a further embodiment of the method of creating an indoor confidence level, wherein the distance being unreliable provides an increase in the indoor confidence level.

In a further embodiment of the method of creating an indoor confidence level, wherein the process captures data at a first time and stores a location and a time in a database, and wherein a data point, captured at a different day, at the same time of day, is compared to the location of the first time, and wherein if the location is the about the same, then the indoor confidence level is increased.

In a further embodiment of the method of creating an indoor confidence level, wherein the indoor confidence level is modified due to any of the additional steps, the indoor confidence level is a medium confidence level.

In a further embodiment of the method of creating an indoor confidence level, wherein the medium confidence level being defined, and wherein the location accuracy radius overlaps with any building, the location is reported as in that building.

In a further embodiment of the method of creating an indoor confidence level, wherein the medium confidence level being defined, and wherein the location accuracy radius does not overlap with a building, the location is reported as not within in that building.

A method for identifying an optimization priority for a given measurement within a dataset of wireless measurements comprising: calculating a score for optimization priority of a signal measurement within a measurement set wherein one of four scenarios exist namely: wherein (1) the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and lower than the actual RS SNR; (2) the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and higher than the actual RS SNR; (3) the slope of RS SNR_([AVERAGE]) is higher than the slope of RS SNR_([IDEAL]) and lower than the actual RS SNR; (4) the slope of RS SNR_([AVERAGE]) is higher than the slope of RS SNR_([IDEAL]) and higher than the actual RS SNR.

In a further embodiment of the method for identifying an optimization priority, wherein the measurement comprises calculating: The formula is defined for Priority (%) is defined by claim 15.

In a further embodiment of the method for identifying an optimization priority, wherein the measurement comprises calculating: The formula for Priority (%) is defined by claim 16.

In a further embodiment of the method for identifying an optimization priority, wherein the measurement comprises calculating: The formula for Priority (%) is defined by claim 17.

In a further embodiment of the method for identifying an optimization priority, wherein the measurement comprises calculating: The formula for Priority (%) is defined by claim 18.

In a further embodiment of the method for identifying an optimization priority, further comprising calculating an indoor confidence level according to claim 1, and wherein a measurement for defining the optimization priority further comprises an indoor confidence level and a location.

In a further embodiment, a method of defining an optimization parameter for wireless network solutions comprising, defining a result, wherein the result is offered in the form of a calculated score ranging from 0% to 100% which represents the normalized deviation of a measurement's signal level and quality from a) a calculation of the area average signal level and quality and b) a calculation of the ideal signal level and quality achievable; wherein the calculated score also uses predefined signal quality value thresholds to place higher priority for optimization on areas with considerably degraded quality; and wherein areas with a high calculated score value and a high signal and poor quality, a performance modification selected from network changes such as antenna configuration or handoff settings adjustments, rather than the installation of a new cell site.

In a further embodiment, a method of optimizing a network carrier for a user comprising: defining at least one location point having a location coordinate (latitude and longitude), defining the location point on a map comprising known structures, said structures defined as polygons on said map, overlying a map of carrier coverage to said map and defining a coverage rate for said location point. In a further embodiment, the method comprising defining at least two location points on a map. In a further embodiment, the method comprising applying at least two different carrier overlays to define an optimized carrier for the location point or location points.

Each of the above embodiments can be combined with one or more of additional elements or exclude an element from one description in order to facilitate the calculation of an ICL or of generating an optimization priority as defined and explained herein.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A and 1B provide a flowchart of a process for improving network service assessment through generation of an indoor confidence level.

FIG. 2 details an example map of structures (buildings) and outdoor spaces for charting inside or outside performance metrics.

FIG. 3 details correlation mapping of RS SNR as compared to Average RS SNR, when plotted against the signal to noise ratio in the y-axis and the signal level in the x-axis.

FIG. 4 details an LTE optimization priority chart, outlining different regions of score ranges and their bounds.

FIG. 5 details a graphical representation of the scenario where the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and actual RS SNR.

FIG. 6 details a graphical representation of calculations wherein the slope of Average RS SNR_([ACTUAL]) is greater than the slope of RS SNR_([IDEAL]) and actual RS SNR is below Average RS SNR.

FIG. 7 details a graphical representation of the scenario wherein the slope of the Average RS SNR_([ACTUAL]) is lower than the slope of RS SNR_([IDEAL]) and actual RS SNR is above Average RS SNR.

FIG. 8 details a graphical representation of calculation for the scenario wherein the slope of the Average RS SNR_([ACTUAL]) is greater than the slope of the RS SNR_([IDEAL]) and actual RS SNR is above Average RS SNR.

FIGS. 9A-9D depict a flowchart showing a detail of calculating an indoor confidence level.

FIG. 10 depicts a flowchart showing a method of use of systems described herein to evaluate network service and for identifying networks having better or worse service for an individual user.

DETAILED DESCRIPTION OF THE INVENTION

For decades, wireless network coverage and performance has been assessed though one-time or periodic collection of measurement samples using professional test equipment. Measurements may include signal level, signal quality, dropped calls, and data transfer speeds. The test equipment is typically installed in a vehicle which is driven on roads or placed in a backpack or cart which is walked through an outdoor or indoor area under study. Location information may be gathered through a GPS (or similar) device connected to the test set, or the location may be manually recorded periodically by the tester. After collection, the samples are processed, displayed, and analyzed on a map to identify any service problems and their geographic location. For example, a sample data set comprising a plurality of data points 45 are depicted in FIG. 3, which are collected in this manual manner.

Measurement sample collection using professional test equipment that is driven or walked through an area can be very costly. Additionally, professional test equipment is limited to a small number of test devices, fragile cables, and connectors that are prone to breakage, and is limited to locations that are accessible to the tester. For example, coverage samples taken from a vehicle may not accurately reflect the actual coverage at locations adjacent to the roadway, or do not accurately reflect the coverage at an elevated height, i.e. inside of a building that is adjacent to that street. Certainly, these may not address coverage underground, such as in subway or trolley spaces, if these are not specifically tested. This leads to weaknesses in the data, lowering its value, despite the high costs of collection.

Another common network service assessment technique involves analysis of measurement samples gathered continuously by the wireless network equipment itself. To locate where problems are occurring geographically with this method, delay measurements from serving and neighboring cell sites may be combined to yield an approximate location of any service problems. Since samples collected directly by a network may be located geographically based on low resolution delay measurements, this often results in poor location accuracy. Additionally, with this method, samples are only available for the host network and not competing networks.

These collection strategies and data points can be further combined together to generate a more robust classification system, but such system is an expensive functionality of two collection methods and neither system nor the combination of collection systems remedies all of the deficiencies regarding such collection methods.

The recent proliferation of smartphones creates another source of measurement samples: applications running in the background or foreground on handheld devices. These devices include a variety of sensors to determine location, including satellite-based systems such as GPS, barometric pressure sensors, and connectivity to the operating system manufacturer's proprietary system for determining the location based on nearby WiFi access points. These devices are often located in places difficult to access with professional test equipment, such as homes and offices. However, the accuracy of a reported location may vary dramatically depending on obstructions in the line of sight to GPS satellites, availability of reference WiFi networks, and other factors.

As networks mature and more network infrastructure is deployed, wireless users are becoming increasingly satisfied with outdoor and in-vehicle service, but they remain unsatisfied with indoor network performance due to building penetration losses suffered during signal reception and transmission. Because of the above limitations, it is difficult for wireless network operators and others to continually assess network coverage and performance at a low cost and high location accuracy in a wide area, especially in buildings.

Accordingly, new techniques are necessary to assess network coverage and performance. Herein are described techniques to overcome existing limitations by identifying which coverage and performance measurement samples are likely indoors or outdoors, attributing the likely indoor samples to particular buildings, aggregating one or more measurement samples to assess network coverage, performance, user density, and data usage per building or outdoor area, and prioritizing areas of potential network service improvement and sales opportunity. Measurement samples may be gathered through applications typically installed on smartphones or gathered directly from wireless networks. Results can be displayed on a map and in tabular form.

One technique is to calculate an Indoor Confidence Level (ICL) for each sample or group of samples. The ICL indicates the likelihood that the sample was generated indoors. Once ICL has been calculated, the samples with high ICL may be filtered and aggregated to yield the overall (mean, median, etc.) performance in matched buildings or outdoor areas.

Indoor Confidence Level

Consumers remain disappointed with service coverage within buildings. Accordingly, it is of interest to wireless network operators, network infrastructure owners and operators, building owners, market research firms, and others to assess network performance, user behavior, and areas of potential improvement in indoor and outdoor areas. Today, measurements of network quality and user behavior may be gathered in the background or foreground of various device applications, such as smartphone games and other applications, or directly from network infrastructure performance counters and call traces. A measurement sample typically includes a location along with an error value. However, there is no reliable indication whether the sample was generated indoors or outdoors. Additionally, if there are multiple buildings in the area, there is no indication which building the sample was recorded in.

Our techniques overcome these limitations by assigning an Indoor Confidence Level to each sample or group of samples. Depending on the use case, a threshold may be used to select only samples with high or low Indoor Confidence Level. Additionally, the samples are attributed to particular buildings. For example, by using data points that are only high ICL, overall coverage, performance, and user density can be assessed for particular buildings or sections of buildings.

The methods below describe a method for determining the likelihood that a device or network-generated measurement sample was generated indoors, and attributes them to a specific building or outdoor area.

FIGS. 1A and 1B provides an overview of the ICL methodology, and FIG. 2 details an example of certain data points on a sample map. Beginning with FIG. 1A a flowchart decision tree depicts a process for evaluating ICL. Step 1 of FIG. 1A comprises receiving location and error values from a device. Together the location and error values define a measurement sample 1, which is received from a device or network performance counter or call trace that contains a reported horizontal and/or vertical location as well as one or more location accuracy values. Horizontal location accuracy is typically defined as the radius 2 of a circle centered at the reported latitude and longitude coordinates. This imaginary circle indicates that there is a high probability of the true location being within its bounds. The area overlap ratio between the horizontal location accuracy circle and polygons defining horizontal footprints of known structures (buildings) is used to calculate an initial indoor confidence level 4.

Accordingly, using the area of a circle, for example for two datapoints, one with 25% of the area within a polygon means a 25 ICL, and one with 75% of the area within a polygon means a 75 ICL. This level can then be modified based on additional factors. For example:

If the device is connected to a WiFi network, an additional weight is applied to the Indoor Confidence Level 5.

If the device's battery is charging, an additional weight is applied to the Indoor Confidence Level 6.

As depicted in FIG. 1B, if the device has been stationary for a period of time in a location with a high initial Indoor Confidence Level, an additional weight is applied to the Indoor Confidence Level 7.

If the recent reported location is far from the previously reported location for the device to have traveled in the elapsed time, an additional weight is applied to the Indoor Confidence Level 8.

If the reported location of previous measurements at similar times of day indicates a high Indoor Confidence Level, an additional weight is applied to the Indoor Confidence Level 9.

In certain embodiments, if the calculated Confidence Level (meaning the initial ICL and the above steps 5-9 are completed to generate a calculated Confidence Level) is above a particular threshold (40% or 50% for example) and within a pre-defined margin from a nearby building, then that building is identified as the one the reported measurement was recorded within 10. These steps are examined in greater detail below.

These steps can be visualized in view of FIG. 2, wherein the horizontal location accuracy values are depicted as circular representations, with the horizontal location accuracy value being defined as the magnitude of the depicted radius. For example, in FIG. 2, there are three different measurement points 26, 27, and 31, each having different accuracy values, and thus the radius of each of 21, 25, and 29 are different, a smaller radius defining a higher location accuracy. Thus, the location accuracy value for the first radius 21 at the first point 26 is much larger than the location accuracy value of the second radius 25 at the second point 27, and the location accuracy value for the third radius 29 at the third point 31 has a location accuracy between the first radius 21 and the second radius 25. These locations are then mapped against the Structures 22, 24, and 28. The words “structures” and “buildings” are used interchangeably herein. Thus, a location point can be determined as an initial threshold based on these known points and measurements.

Thus, the overlap radius of each of the first radius 21, the second radius 25, and the third radius 29 are overlapped on the polygonal map to generate an initial indoor confidence level 4. It is important to define this indoor confidence level, as optimization of the network may vary depending on indoor or outdoor performance.

Therefore, for a given data point, an indoor confidence level is determined to be on a continuum between 0% and 100%, with 0% defining that there is no indoor confidence, i.e. the data point is likely outdoors. By contrast a score of 100% defines that the data point is likely indoors. The points in between define the continuum wherein there is not a binary response, but a level of certainty with regard to the position of a device when taking a measurement. Indeed, FIG. 2 defines some examples of these confidence levels, with the first data point 26 having a 50% of the area of the circle indoors and 50% outdoors, and thus defines an indoor confidence level of 50. A second data point 27 having 0% of area overlap with any building polygon indicates 0% indoor initial confidence level, and third data point 31 having a 100% of the area inside a building polygon, defining a 100% initial indoor confidence level.

Determining and refining the initial indoor confidence level for the first data point 26 is a key feature that is addressed by the methods defined herein. Once the horizontal location accuracy values are defined and the radius 21 plotted against the polygonal map, in this case, depicting one half of the radius 21 within 33 the structure 22, and one half outside 32 of the structure 22, an initial ICL of 50% is calculated Improvement of this level can be made through additional steps.

Thus, as defined in FIG. 1A a further step to refining the initial indoor confidence level is to ask whether the data point 26 was collected when the device (any device, whether it is a device having cellular telemetry [phone, tablet, laptop, watch, etc.], a network performance counter, or call trace) is also connected to a WiFi network 5. This is a relevant data point as many devices are not connected to WiFi when traveling on a road or walking through a neighborhood, and these individuals typically use cellular data for connections. Thus, a person using a smart phone and out for a run listens to music through a streaming service by using cellular data. When the device does connect to WiFi 5 it usually means that the device is now in a location that is ordinarily an indoor location, such as a home, office, gym, or other space where a WiFi network that is recognized by the device is expected. Thus, a strong active or passive WiFi connection indicates a higher confidence level of the device being indoors at that moment.

Where no WiFi network is detected, further steps may be evaluated to determine an indoor confidence level. In certain embodiments, the lack of a WiFi network can be utilized to reduce the indoor confidence level. Whether a decrease in the indoor confidence level is provided or no change is provided, further steps can be utilized to further modify the indoor confidence level.

When placed indoors, devices are frequently connected to a charger in order to maximize battery life of the device. While it is possible to do this in an outdoor setting, i.e. with an extended battery or power pack, the presence or absence of charging is indicative of the location of the device. Furthermore, many of the battery pack devices are capable of detecting the differences between a connection to an AC charger or another type of mobile charging. Accordingly, an increase in the indoor confidence level adjustment is generated if the device is charging. In preferred embodiments, an increase of the ICL is defined only when the charge is defined as coming from AC power. Thus, supplemental battery packs or mobile charging would not generate an increase in the ICL based on this criterion.

Again, the absence of charging needs not reduce the indoor confidence level in all circumstances, as it is common to also have a device not charging if the device is in use, or simply because there is no access at the device's present location to charge.

The indoor confidence level can again be increased if a device is stationary at a high confidence location 7. A high confidence location is one that has an ICL of at least 50%, or which has previously been categorized as being indoors based on additional data. For example, data point 26 would have its indoor confidence level increased if the device remains stationary for a given amount of time. That would indicate that the device may be within the building, for example, at a desk, in a locker, or on a person as they are working within a building. It is more likely that a device is indoors when stationary as compared to outdoors and because of the propensity for the device to be with a person who is required to be working at that location.

Many devices have GPS tracking or other triangulation positional tracking systems. However, these tracking systems have somewhat limited accuracy and are prone to wide positional deviations from time to time. For example, a device in a static location may measure its location every “n” seconds, with “n” being usually between 1 and 60 seconds. Of these measurements, only a subset might have nearly identical locations, with the rest reporting high variance in locations. If the distance between the nearly identical locations and the remaining ones is higher than the user could have travelled in the short period of time, then there is a high likelihood that the device is not actually moving but is instead experiencing location accuracy issues due to a poor GPS lock or network-assisted location triangulation. Accordingly, the indoor confidence level is increased where the distance is significantly different than from a previous location 8, because there is a higher probability that the device is static at a location that is not using GPS, as indoor applications have a lower use of GPS and higher use of triangulation protocols, and these triangulation protocols are more prone to deviations in position than their GPS counterparts, specifically when inside of a building.

Accordingly, as defined in FIG. 1A, steps 1-4 define an initial ICL and the remaining steps seek to increase the certainty of the ICL calculation, whether that is lower or higher. Accordingly, in addition to the mapping on the polygonal map and determining error values for a particular location, the additional qualification steps in steps 5-9 of FIGS. 1A and 1B are further defined below. These steps may be used alone or in combination with one another.

After defining an initial ICL, based upon mapping then one or more of the following steps can be utilized to evaluate confidence in the ICL determination: connection to WiFi 5, battery charging 6, stationary device 7, distance traveled from a prior position 8, and location of device at a similar time and place from a prior recorded location 9. Each of these factors provides information relevant to the position of a device, and whether it is inside of a building.

Furthermore, prior data based on location and timing can also be utilized to modify the ICL. For example, historical data showing a measurement at a similar time and place 9 during several weeks of data would be highly suggestive of an indoor location, i.e. the place of employment of the individual. However, a different location at a time and place than normally reported indicates the possibility of being at a different indoor location or an outdoor location, thus reducing the ICL.

Accordingly, as in Step 7, we can define a high confidence location through step 9, which compares the previous location at a similar time of day. In a preferred embodiment, the location and time are stored within a database or in memory, which allows for a comparison from a time point “to” to a present time, for comparison to attain the high confidence location determination. For example, places where the phone typically stays are at home, school, or work, and these can be tracked through simply recurring habits based on time and location to generate such high confidence locations. Accordingly, as in step 10, below, we can also attribute a high confidence location to a building, just as below a medium confidence is attributed to a building. Thus, any time a high confidence is set, and the measurement error is within a building polygon, we set the location as in that building, just as in steps 10, 11, and 13 with medium confidence listed below. If there is no building nearby, then that indicates a likelihood that there is not a high confidence to begin with and that the previous time and location may be outdoors, such as someone who runs at the same time every day.

Therefore, taking one or more of the above steps into consideration, a medium confidence level can be calculated. The system is queried in step 10: Is medium confidence level location within nearby building borders? By this, we mean that “medium confidence” refers to an analysis of steps 5-9 of FIGS. 1A and 1B. Medium confidence itself can be a variable and designated based upon the criteria of the search. For example, wherein medium confidence is defined as meeting at least one of the evaluation steps. Namely, if in step 5, there was a WiFi connection, or in step 6, charging, or step 7 stationary device, or step 8 the distance travelled, or step 9 a similar location. Each of these allows for increasing the confidence, and thus any positive responses to such evaluations steps would result in medium confidence. However, because medium confidence is intended to be a variable, medium confidence may require a positive assertion to two or more of the steps 5-9, or three or more, or even four or more, or all five. Preferably, two or more steps are utilized to generate a medium confidence. Accordingly, if medium confidence is defined, and within nearby building borders, then the answer is “Yes” 12, then the location is reported as being within the building 14. If the answer is “No” 13, either because of a failure of medium confidence or of being within nearby building borders, then the location is reported as not within the building 15. Thus, depending on the answer, a location and indoor confidence level is returned 16 and can be stored in a database or utilized for further analysis. Thus, step 15 confirms both a determination of inside or outside of a building and the confidence level returned of that determination.

In other embodiments, confidence level is additive of the initial confidence level. Namely, an initial score is generated by mapping and then an increase or decrease in the score is generated. For example, for each positive assertion to steps 5-9, ten points can be added to the initial ICL. In other embodiments, twenty-five points can be added to the initial ICL. In other embodiments fifty points can be added to the initial ICL. Accordingly, users can modify the scoring based upon particular certainty with regard to the data to fit their needs. Furthermore, a positive assertion to steps 5-9 may increase the initial ICL, but a negative response may also decrease the initial ICL by an amount. Each step may also have different weights than another step.

For example, where a plurality of these indoor confidence level scores is generated, a map can be created of signal to noise ratios and signal strength using only measurements with high or low ICL to allow for improved data management of signal quality in a particular area. A high ICL may be greater than 50, 60, 70, 80, or 90, and a low ICL may be below 50, 40, 30, 20, 10, or any number in-between for either the high or low ICL. We can use this information alone or in conjunction with historical or legacy data to generate improved signal quality. This information can then be utilized, on one hand, by service providers who can evaluate weaknesses in their signal reach or quality to specifically improve the infrastructure, instead of deploying additional equipment to increase the signal level in the impacted area. Those in the industry will recognize that the data herein can be mined to allow for optimization rather than capacity expansion.

Furthermore, this information can be utilized within a series of publications, whether online or hard copy, to generate thematic maps regarding signal quality. One particular embodiment utilizes a geographic map, similar to that of FIG. 2, wherein a user can input their location into an electronic database. FIG. 10 depicts a flowchart showing this process comprising defining an electronic map 131, defining one or more carriers on the map 132, defining a location or inputting a location of a user 133 on said map, comparing the defined location of the user 133 with the carrier coverage 134, and changing the coverage to different providers 135 to optimize the carrier coverage. This last step can be performed automatically by the system; for example, the defined coverage 132 may be an input to a current carrier of the user, and, once the user defines her location 133, the system can automatically select an optimized carrier 135 to be displayed on the map. It may be that several carriers have excellent signal in the particular location, so it may be advantageous to generate two or more location points 136 for the user and then re-comparing or re-calculating an optimized provider 137. These steps can be repeated as necessary with “N” number of location points in order to define an optimized carrier. For example, points might be home, school, job, friend's home, etc., and a user can then optimize the carrier coverage based upon these points. Obviously, the data can then be mined by the providers to identify possible targets for sales or for areas that have the most desire and can be optimized by a carrier.

This can be used by both the user and by carrier providers, as the user can find optimal service for the best price, and the providers can identify weaknesses in their service to be improved. Furthermore, providers can identify areas of strength against their competition and focus advertising to these areas to capture additional sales.

FIGS. 9A, 9B, 9C, and 9D provide a detailed flowchart for calculating of elements of Indoor Confidence Level, and details setting of certain levels of confidence as the process and assertion steps are completed.

In view of FIG. 9A, starting the process at 100, we take a data point and generate a circular “measurement buffer” with a radius equal to the “measurement location accuracy” 101. An initial threshold question is whether this “measurement location” is inside any building polygon 102. If yes, we set the indoor confidence level to 100% 103. If no, we continue with additional calculations 104.

A further step is to calculate an overlap area 105 as overlap between a measurement buffer and all building polygons. Second, we calculate distance traveled as a distance difference between current and previous measurement location 106. Next, we measure the time traveled as a time difference between current and previous measurement location 107. Finally, we take the average location as the location coordinates of average measurement location of the device for the time (hour) of day from historic values for the same device 108.

Each of these steps above provides value to measure the confidence of whether a data point is generated inside or outside of a building. Considering the overlap area, the next step is determination of whether this overlap area is greater than 50% 109. If yes, then the next step is to determine whether the device is mobile charging 112. If yes, then indoor confidence level is set to 100% 103, then the measurement location is defined as within a “location threshold 1” of any building polygon edge. However, if the overlap area is less than 50%, then we consider whether the overlap area is between 40% and 50% 110. If yes, then the measurement location is defined as within a “location threshold 1” of any building polygon edge. The status of mobile charging 112 is then considered.

However, if the mobile charging 112 is no, then the process asks whether there is a WiFi connection 113, wherein no WiFi continues to point D, and yes asks whether WiFi Signal strength is greater than a WiFi signal threshold 114, in which yes sets indoor confidence level to 100% 103, and no leads to E.

Following path D, if the distance traveled is less than the “distance threshold 1” 115 then the system asks whether the time traveled is less than the “time threshold 1” 116. Where the distance traveled is not less than the distance threshold from 115, then the system asks whether the distance traveled is greater than the “distance threshold 2” in 117. If yes, then we ask if the measurement location is within the “location threshold 2” from the average location 118. If the time travelled is less than “time threshold 1” 116 then we set adjustment coefficient to “distance coefficient 1” 121: if the time is not less, then we proceed to determine whether the measurement location is within the location threshold 2 from the average location (118). If yes, then we set adjustment coefficient to location coefficient 1 (123). If no, then we set adjustment coefficient to location coefficient 2 (122). Finally, we set the indoor confidence level equal to the overlap area times the adjustment coefficient 124 which was set in the previous steps (119, 120, 121, 122, 123) leading to the aforementioned product. This takes us to the end of the algorithm decision tree 125.

In each of these cases, the adjustment coefficient can be set by the particular stringent requirements of the process at hand. Accordingly, in one embodiment, the value may be X and, in another embodiment, the value Y. Furthermore, the coefficients do not need to be identical, and certain coefficients may hold more weight than others.

Therefore, preferred embodiments define a method of defining an indoor confidence level through a process including steps of: (1) receiving location and error values from a device, wherein error values are equated to location accuracy results; (2) comparing the location and accuracy radius to a map of known buildings and outdoor locations, (3) overapplying the accuracy radius with buildings to generate an initial indoor confidence level. In certain embodiments, these steps are sufficient for generating an indoor confidence level.

In additional embodiments, it is necessary to include at least one or more steps, selected from the group consisting of: detecting whether a device is connected to a WiFi network; detecting whether the device battery is charging, detecting if the device is stationary at a high confidence location, detecting whether the device moves a significant distance in a period of time “T” that provides indication of unreliable location mapping; detecting a location and comparing to a similar time and location from a previous day; and combinations thereof.

In further embodiments, using any one of the above steps generates a medium confidence score that then detects whether the data is within nearby building borders and then determining from this point whether the location is indoors or outdoors and returning a location and indoor confidence level.

In further embodiments, once a high confidence location is determined, a location and indoor confidence level can be returned that reports that the location is within a building. For example, where the location accuracy radius is within a building polygon and a high confidence location, then the location is reported as within that building.

Where a high or medium confidence level is determined and a location accuracy radius touches two or more building polygons, the building location is determined to be the building polygon with the greatest percentage within the location accuracy radius.

Optimization Priority Level

Calculation of the Indoor Confidence Level has several benefits that are not currently utilized by users or carriers. However, additional data can be mined and utilized alone or in combination with the indoor confidence level. An Optimization Priority Level (OPL) may be calculated for each sample or group of samples. The OPL indicates the potential for coverage or performance improvement through network optimization changes such as cell site antenna configuration changes or handover settings adjustments. OPL from multiple samples may also be filtered and aggregated to yield the overall (mean, median, etc.) need for improvement through network optimization changes.

Once ICL or OPL values are calculated, several individual raw measurements in the same general location or building can be aggregated to assess the overall service in each building or outdoor area. The ICL and/or OPL values may then be combined with each other or with other inputs such as the percentage of measurements on low bands compared to high bands, density of users, proportion of users on legacy vs. newer technologies (LTE vs. UMTS, for example), and others to rank buildings and outdoor areas for network improvement and sales opportunity.

Optimization Priority

An important task faced by wireless network operators is improvement of network signal level (for example, LTE RSRP [Reference Signal Receive Power]) with little to no cost to signal quality (LTE RS SNR [Reference Signal Signal-to-Noise Ratio], for example). These are two of several different Key Performance Indicators (KPIs), and which these KPI's typically follow an inverse relationship. Currently this can be accomplished during:

Network Planning Phase: Using signal propagation and modeling tools;

Post-launch Optimization Phase: RF measurements collection using a dedicated drive-test apparatus and manual processing and analysis of data logs; and

Mature Network Optimization Phase: Application-generated or network-gathered RF measurements collection from a large number of existing customers and automatic processing and visualization.

The optimization methods outlined above rely on manual identification of areas of good signal level and poor signal quality with the added challenge of individual toggling between LTE RSRP and LTE RS SNR and/or LTE RSRQ maps in the case of an LTE network. The same concept applies to all modern wireless technologies, including 5G, UMTS, CDMA, etc.

Presented below is a proposed alternative to existing signal level and quality optimization techniques which offers the ability to identify areas of significant imbalance of signal level and quality. The result is offered in the form of a score ranging from 0% to 100% which represents the normalized deviation of a measurement's signal level and quality from a) the area average signal level and quality and b) the ideal signal level and quality achievable. This calculated score also uses predefined signal quality value thresholds to place higher priority for optimization on areas with considerably degraded quality. In general, areas with a high Optimization Priority value have high signal level but poor quality, which may be improvable through low-cost network changes such as antenna configuration or handoff settings adjustments, rather than the installation of a new cell site. Several examples are provided below with regard to LTE optimization protocols, each of which can be utilized to identify optimization priority. However, LTE optimization may be exchanged for 3G, 4G, 5G, or other standard as appropriate using the same formulae provided.

The area average signal level and quality metric defines the Average RS SNR as a linear function of RSRP. The equation for this line, in particular its slope and intercept, can be determined by finding the best fitting straight line on an RSRP vs RS SNR scatter plot.

The ideal signal level and quality achievable metric (here, with regarding to LTE) is found by first establishing the relationship between RSRP and RS SNR (from the Downlink Power Budget calculation at the Receiver [UE]), or from RSRQ: Reference Signal Signal-to-Noise Power (RS SNR) [dBm]=Reference Signal Received Power (RSRP) [dBm]−Noise Power (N) [dB] Where: Noise Power (N) [dB]=Thermal Noise Power+Receiver Noise Figure+Channel Noise Receiver Noise Figure=7 [dB] Thermal Noise Power=White Noise Power Spectral Density (N _(o))×Sub-carrier Bandwidth (W)=−174 [dBm/Hz]+10*log(15,000 [Hz])=−132.25 [dBm]

Ideally, with Channel Noise equal to zero, the calculated relationship between LTE RSRP and LTE RS SNR can be reduced to: RS SNR_([IDEAL])=RSRP_([ACTUAL])−125.25 [dBm]

FIG. 3 provides an example distribution of LTE RSRP and RS SNR measurements 44 collected using drive-test equipment with calculated RS SNR_([IDEAL]) 43 and Average RS SNR_([ACTUAL]) 44 computed over the entire sample set, with RS SNR (dB) 41 defined on the y-axis and RSRP (dBm) 42 defined on the x-axis.

To prioritize optimization activities, LTE RS SNR measurements with highest delta from ideal RS SNR (RS SNR_([IDEAL])) and cluster average RS SNR (Average RS SNR_([ACTUAL])) can be grouped into several sets (10, for example) yielding equal ranges of normalized scores in 10% increments from 0% to 100%. These ranges are depicted in FIG. 4. The groups of normalized scores can be partitioned based on absolute RS SNR thresholds (−20 dB to −10 dB, −10 dB to 0 dB, etc.) since signal quality has a higher impact than signal level in facilitating high performance of services provided by the wireless carriers.

FIG. 4 provides a visual overview of example thresholds and regions of assigned LTE Optimization Priority, comprising actual data points plotted against RS SNR over RSRP. Ultimately, the optimization, as calculated by the formula are detailed in FIG. 4, which defines optimization priority.

Calculation Methodology

An LTE Optimization Priority score can be calculated for individual measurements of LTE RSRP and corresponding LTE RS SNR values from a set of measurements by way of simple linear functions definition and extrapolation. It is important that the measurements set contains a large number of LTE RSRP and LTE RS SNR samples with a wide variance of values.

There are four possible scenarios that need to be considered which define the formulae for score calculation depending on the slopes of RS SNR_([IDEAL]) and Average RS SNR_([ACTUAL]) as well as the value of RSRP_([ACTUAL]) with respect to the line defined by the Average RS SNR_([ACTUAL]). Step-by-step formula definitions are provided for each scenario below:

The following formulae define the situation wherein: the priority base=any percentage between 0 and 90 and defines the parameters of the delta min and delta max. This allows the scenario for wherein the delta is equal to actual RS SNR minus ideal RS SNR.

Formula:

The formula for Priority (%) is defined as: Priority (%)=Priority_(BASE)+Priority_(INCREMENT)  [1] Where:

$\begin{matrix} {{Priority}_{BASE} = {{Any}\mspace{14mu}\left( {{10\%},{20\%},{30\%},{40\%},{50\%},{60\%},{70\%},{80\%},{90\%}} \right)}} & \lbrack 2\rbrack \\ {{Priority}_{INCREMENT} = {0.1 \times \frac{{{abs}(\Delta)} - {{abs}\left( \Delta_{M\;{IN}} \right)}}{{{abs}\left( \Delta_{{MA}\; X} \right)} - {{abs}\left( \Delta_{M\;{IN}} \right)}}\mspace{14mu}{such}\mspace{14mu}{that}\text{:}}} & \lbrack 3\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{M\;{IN}}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {0\%}}}} & \lbrack 4\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{M\;{AX}}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {10\%}}}} & \lbrack 5\rbrack \\ {\mspace{79mu}{\Delta = {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {{RS}\mspace{14mu}{SNR}_{IDEAL}}}}} & \lbrack 6\rbrack \end{matrix}$ Let: a) Average RS SNR_(ACTUAL) be directly proportional to Average RS SNR_(ACTUAL) as a linear function y=m*x+a and defined as: Average RS SNR_(ACTUAL) =m _(AVERAGE RS SNR)×RSRP_(ACTUAL) +a _(AVERAGE RS SNR)  [7] b) RS SNR_(IDEAL) be equal to RSRP_(ACTUAL) offset by 125.2 defined as: RS SNR_(IDEAL)=RSRP_(ACTUAL)+125.2  [8] c) Average RS SNR_(ACTUAL) have a slope smaller than RS SNR_(IDEAL) defined as: m _(AVERAGE RS SNR) _(ACTUAL) <m _(RS SNR) _(IDEAL)   [9] d) RS SNR_(ACTUAL) be between RS SNR_(Threshold1) (point y₂ on y-axis) and RS SNR_(Threshold2) (point y₃ on y-axis) defined as: RS SNR(y ₂)=RS SNR_(Threshold1)≤RS SNR_(ACTUAL)  [10] and RS SNR_(ACTUAL)<RS SNR(y ₃)=RS SNR_(Threshold2)  [11] e) RSRP_(ACTUAL) be between RSRP_(Threshold1) (point x₁ on x-axis) and RSRP_(Threshold2) (point x₃ on x-axis) defined as: RSRP(x ₁)=RSRP_(Threshold1)≤RSRP_(ACTUAL)  [12] and RSRP_(ACTUAL)<RSRP(x ₃)=RSRP_(Threshold2)  [13] Then: The difference between RS SNR_(ACTUAL) and RS SNR_(IDEAL) is defined as Δ: Δ=RS SNR_(ACTUAL)−RS SNR_(IDEAL)=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [14] In combination with [8], Δ can be simplified as: Δ=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [15] The minimum distance (Δ_(MIN)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₂, y₂) on line Average RS SNR_(ACTUAL), such that: Δ_(MIN)=Δ(x ₂ ,y ₂)=Average RS SNR_(ACTUAL)(x ₂)−RS SNR_(IDEAL)(x ₂)  [16] Using the definition in [7]:

$\begin{matrix} {{{Average}\mspace{14mu}{RS}\mspace{14mu}{{SNR}_{ACTUAL}\left( x_{2} \right)}} = {{{m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times {{RSRP}\left( x_{2} \right)}} + a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} = {{RS}\mspace{14mu}{{SNR}\left( y_{2} \right)}}}} & \lbrack 17\rbrack \\ {\mspace{79mu}{{{RSRP}\left( x_{2} \right)} = \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{2} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}}} & \lbrack 18\rbrack \end{matrix}$ Using the definition in [8]: RS SNR_(IDEAL)(x ₂)=RSRP(x ₂)+125.2  [19] Combining terms found in [17], [18] and [19] into [16] results in:

$\begin{matrix} {\Delta_{M\;{IN}} = {{{RS}\mspace{14mu}{{SNR}\left( y_{2} \right)}} - \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{2} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} - 125.2}} & \lbrack 20\rbrack \end{matrix}$ Using the limit defined in [10] and re-arranging [20] results in:

$\begin{matrix} {\Delta_{M\;{IN}} = \frac{\begin{matrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}} & \lbrack 21\rbrack \end{matrix}$ The maximum distance (Δ_(MAX)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₃, y₂), defined as: Δ_(MAX)=RS SNR(y ₂)−RS SNR_(IDEAL)(x ₃)  [22] Using the relationship between RS SNR_(IDEAL) and RSRP_(ACTUAL) as defined by [8] at point (x₃, y₂) and the limit of RS SNR_(ACTUAL) [10] and RSRP_(ACTUAL) [13] results in: RS SNR_(IDEAL)(x ₃)=RSRP_(ACTUAL)(x ₃)+125.2  [23] Δ_(MAX)=RS SNR_(Threshold1)−RSRP_(Threshold2)−125.2  [24] Yielding: Combining terms found in [15], [21] and [24] into [3] results in:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{\begin{matrix} {{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right)} -} \\ {{abs}\left( \frac{\begin{matrix} {m_{{\,{Avearge}}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)} \end{matrix}}{\begin{matrix} {{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - {RSRP}_{{Threshold}\; 1} - 125.2} \right)} -} \\ {{abs}\left( \frac{\begin{matrix} {{\, m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)} \end{matrix}}}}} & \lbrack 25\rbrack \end{matrix}$ With numerator and denominator simplification in [25]:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{\begin{matrix} {{{abs}\begin{pmatrix} {m_{{RS}\mspace{14mu}{SNR}_{AVERAGE}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right) \end{pmatrix}} -} \\ {{abs}\begin{pmatrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}} \end{matrix}}{\begin{matrix} {{{abs}\begin{pmatrix} {m_{{RS}\mspace{14mu}{SNR}_{AVERAGE}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - {RSRP}_{{Threshold}\; 2} - 125.2} \right) \end{pmatrix}} -} \\ {{abs}\begin{pmatrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}} \end{matrix}}}}} & \lbrack 26\rbrack \end{matrix}$

The calculations of the above formulae are then graphically represented by FIG. 5, depicting the scenario where the slope of RS SNR_([AVERAGE]) is lower than the slope of RS SNR_([IDEAL]) and actual RS SNR.

Accordingly, we see that RS SNR_([IDEAL]) 78 has a first slope and is parallel to slope 80. However, Average RS SNR_([ACTUAL]) slope 79 is lower than the slope 78 of RS SNR_([IDEAL]). This provides for data the delta min 81 and the delta max 82 points. This creates a section between y₂ 73 and y₃ 74 between the points of delta min 81 and delta max 82 that defines the location of the datapoint 83 priority under consideration.

A further set of formulae to calculate wherein the slope of Average RS SNR_([ACTUAL]) is greater than the slope of RS SNR_([IDEAL]) and actual RS SNR is below Average RS SNR. A graphical representation of this solution is depicted in FIG. 6. Herein, the delta min 92 is from point y₃ to a point on RS SNR_([IDEAL]) at x₂, and the delta max 82 is from y₂ to a point on RS SNR_([IDEAL]) at x₃. The formulae are defined as:

Formula:

The formula for Priority (%) is defined as: Priority (%)=Priority_(BASE)+Priority_(INCREMENT)  [1] Where:

$\begin{matrix} {{Priority}_{BASE} = {{Any}\mspace{14mu}\left( {{10\%},{20\%},{30\%},{40\%},{50\%},{60\%},{70\%},{80\%},{90\%}} \right)}} & \lbrack 2\rbrack \\ {\mspace{79mu}{{Priority}_{INCREMENT} = {0.1 \times \frac{{{abs}(\Delta)} - {{abs}\left( \Delta_{{MI}\; N} \right)}}{{{abs}\left( \Delta_{{MA}\; X} \right)} - {{abs}\left( \Delta_{M\;{IN}} \right)}}\mspace{14mu}{such}\mspace{14mu}{that}\text{:}}}} & \lbrack 3\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{M\; I\; N}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {0\%}}}} & \lbrack 4\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{{MA}\; X}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {10\%}}}} & \lbrack 5\rbrack \\ {\mspace{79mu}{\Delta = {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {{RS}\mspace{14mu}{SNR}_{IDEAL}}}}} & \lbrack 6\rbrack \end{matrix}$ Let: a) Average RS SNR_(ACTUAL) be directly proportional to Average RS SNR_(ACTUAL) as a linear function y=m*x+a and defined as: Average RS SNR_(ACTUAL) =m _(AVERAGE RS SNR)×RSRP_(ACTUAL) +a _(AVERAGE RS SNR)  [7] b) RS SNR_(IDEAL) be equal to RSRP_(ACTUAL) offset by 125.2 defined as: RS SNR_(IDEAL)=RSRP_(ACTUAL)+125.2  [8] c) Average RS SNR_(ACTUAL) have a slope smaller than RS SNR_(IDEAL) defined as: m _(AVERAGE RS SNR) _(ACTUAL) >m _(RS SNR) _(IDEAL)   [9] d) RS SNR_(ACTUAL) be between RS SNR_(Threshold1) (point y₂ on y-axis) and RS SNR_(Threshold2) (point y₃ on y-axis) defined as: RS SNR(y ₂)=RS SNR_(Threshold1)≤RS SNR_(ACTUAL)  [10] and RS SNR_(ACTUAL)<RS SNR(y ₃)=RS SNR_(Threshold2)  [11] e) RSRP_(ACTUAL) be between RSRP_(Threshold1) (point x₁ on x-axis) and RSRP_(Threshold2) (point x₃ on x-axis) defined as: RSRP(x ₁)=RSRP_(Threshold1)≤RSSP_(ACTUAL)  [12] and RSRP_(ACTUAL)<RSRP(x ₃)=RSRP_(Threshold2)  [13] Then: The difference between RS SNR_(ACTUAL) and RS SNR_(IDEAL) is defined as Δ: Δ=RS SNR_(ACTUAL)−RS SNR_(IDEAL)=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [14] In combination with [8], Δ can be simplified as: Δ=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [15] The minimum distance (Δ_(MIN)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₂, y₃) online Average RS SNR_(ACTUAL), defined as: Δ_(MIN)=Δ(x ₂ ,y ₃)=Average RS SNR_(ACTUAL)(x ₂)−RS SNR_(IDEAL)(x ₂)  [16] Using the definition in [7]:

$\begin{matrix} {{{Average}\mspace{14mu}{RS}\mspace{14mu}{{SNR}_{ACTUAL}\left( x_{2} \right)}} = {{{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times {{RSRP}\left( x_{2} \right)}} + a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} = {{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}}}} & \lbrack 17\rbrack \\ {\mspace{79mu}{{{RSRP}\left( x_{2} \right)} = \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{{ACTUAL}\;}}}}} & \lbrack 18\rbrack \end{matrix}$ Using the definition in [8]: RS SNR_(IDEAL)(x ₂)=RSRP(x ₂)+125.2  [19] Combining terms found in [17], [18] and [19] into [16] results in:

$\begin{matrix} {\Delta_{M\; I\; N} = {{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} - 125.2}} & \lbrack 20\rbrack \end{matrix}$ Using the limit defined in [10] and re-arranging [20] results in:

$\begin{matrix} {\Delta_{M\;{IN}} = \frac{\begin{matrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}} & \lbrack 21\rbrack \end{matrix}$ The maximum distance (Δ_(MAX)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₃, y₂), defined as: Δ_(MAX) =y ₂−RS SNR_(IDEAL)(x ₃)  [22] Using the relationship between RS SNR_(IDEAL) and RSRP_(ACTUAL) as defined by [8] at point (x₃, y₂) and the limit of RS SNR_(ACTUAL) [10] and RSRP_(ACTUAL) [13] results in: RS SNR_(IDEAL)(x ₃)−RSRP_(ACTUAL)(x ₃)+125.2  [23] Δ_(MAX)=RS SNR_(Threshold1)−RSRP_(Threshold2)−125.2  [24] Yielding: Combining terms found in [15], [21] and [24] into [3] results in:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{\begin{matrix} {{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right)} -} \\ {{abs}\left( \frac{\begin{matrix} {{\, m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)} \end{matrix}}{\begin{matrix} {{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - {RSRP}_{{Threshold}\; 2} - 125.2} \right)} -} \\ {{abs}\left( \frac{\begin{matrix} {{\, m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{\, m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)} \end{matrix}}}}} & \lbrack 25\rbrack \end{matrix}$ With numerator and denominator simplification in [25]:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{\begin{matrix} {{{abs}\begin{pmatrix} {m_{{RS}\mspace{14mu}{SNR}_{AVERAGE}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right) \end{pmatrix}} -} \\ {{abs}\begin{pmatrix} {{\, m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}} \end{matrix}}{\begin{matrix} {{{abs}\begin{pmatrix} {m_{{RS}\mspace{14mu}{SNR}_{AVERAGE}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 1}} - {RSRP}_{{Threshold}\; 2} - 125.2} \right) \end{pmatrix}} -} \\ {{abs}\begin{pmatrix} {{\, m_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \times} \\ {\left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right) -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}} \end{matrix}}}}} & \lbrack 26\rbrack \end{matrix}$

A further scenario exists wherein the slope of the Average RS SNR_([ACTUAL]) 146 is lower than the slope of RS SNR_([IDEAL]) 78 and actual RS SNR is above Average RS SNR. FIG. 7 then shows a graphical representation of the same, depicting y₂ (141), y₃ (142), limits on the RSRP x₁ (143), x₂ (144), and x₃ (145). Which defines the delta min 147, the delta max 148, and an area of 149. The formulae are defined as:

Formula:

The formula for Priority (%) is defined as: Priority (%)=Priority_(BASE)+Priority_(INCREMENT)  [1] Where:

$\begin{matrix} {{Priority}_{BASE} = {{Any}\mspace{14mu}\left( {{10\%},{20\%},{30\%},{40\%},{50\%},{60\%},{70\%},{80\%},{90\%}} \right)}} & \lbrack 2\rbrack \\ {\mspace{79mu}{{Priority}_{INCREMENT} = {0.1 \times \frac{{{abs}(\Delta)} - {{abs}\left( \Delta_{{MI}\; N} \right)}}{{{abs}\left( \Delta_{{MA}\; X} \right)} - {{abs}\left( \Delta_{M\;{IN}} \right)}}\mspace{14mu}{such}\mspace{14mu}{that}\text{:}}}} & \lbrack 3\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{M\; I\; N}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {0\%}}}} & \lbrack 4\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{{MA}\; X}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {10\%}}}} & \lbrack 5\rbrack \\ {\mspace{79mu}{\Delta = {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {{RS}\mspace{14mu}{SNR}_{IDEAL}}}}} & \lbrack 6\rbrack \end{matrix}$ Let: a) Average RS SNR_(ACTUAL) be directly proportional to Average RS SNR_(ACTUAL) as a linear function y=m*x+a and defined as: Average RS SNR_(ACTUAL) =m _(AVERAGE RS SNR)×RSRP_(ACTUAL) +a _(AVERAGE RS SNR)  [7] b) RS SNR_(IDEAL) be equal to RSRP_(ACTUAL) offset by 125.2 defined as: RS SNR_(IDEAL)=RSRP_(ACTUAL)+125.2  [8] c) Average RS SNR_(ACTUAL) have a slope smaller than RS SNR_(IDEAL) defined as: m _(AVERAGE RS SNR) _(ACTUAL) <m _(RS SNR) _(IDEAL)   [9] d) RS SNR_(ACTUAL) be between RS SNR_(Threshold1) (point y₂ on y-axis) and RS SNR_(Threshold2) (point y₃ on y-axis) defined as: RS SNR(y ₂)=RS SNR_(Threshold1)≤RS SNR_(ACTUAL)  [10] and RS SNR_(ACTUAL)<RS SNR(y ₃)=RS SNR_(Threshold2)  [11] e) RSRP_(ACTUAL) be between RSRP_(Threshold1) (point x₁ on x-axis) and RSRP_(Threshold2) (point x₃ on x-axis) defined as: RSRP(x ₁)=RSRP_(Threshold1)≤RSSP_(ACTUAL)  [12] and RSRP_(ACTUAL)<RSRP(x ₃)=RSRP_(Threshold2)  [13] Then: The difference between RS SNR_(ACTUAL) and RS SNR_(IDEAL) is defined as Δ: Δ=RS SNR_(ACTUAL)−RS SNR_(IDEAL)=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [14] In combination with [8], Δ can be simplified as: Δ=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [15] The minimum distance (Δ_(MIN)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is along the line RS SNR_(IDEAL) thus equal to zero: Δ_(MIN)=0  [16] The maximum distance (Δ_(MAX)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₃, y₃) on line Average RS SNR_(ACTUAL), defined as: Δ_(MAX)=Δ(x ₃ ,y ₃)=Average RS SNR_(ACTUAL)(x ₃)−RS SNR_(IDEAL)(x ₃)  [17] Using the definition in [7]:

$\begin{matrix} {{{Average}\mspace{14mu}{RS}\mspace{14mu}{{SNR}_{ACTUAL}\left( x_{3} \right)}} = {{{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times {{RSRP}\left( x_{3} \right)}} + a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} = {{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}}}} & \lbrack 18\rbrack \\ {\mspace{79mu}{{{RSRP}\left( x_{3} \right)} = \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}}} & \lbrack 19\rbrack \end{matrix}$ Using the definition in [8]: RS SNR_(IDEAL)(x ₃)=RSRP(x ₃)+125.2  [20] Combining terms found in [18], [19] and [20] into [17] results in:

$\begin{matrix} {\Delta_{{MA}\; X} = {{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - \frac{{{RS}\mspace{14mu}{{SNR}\left( y_{3} \right)}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} - 125.2}} & \lbrack 21\rbrack \end{matrix}$ Using the limit defined in [11] and re-arranging [21] results in:

$\begin{matrix} {\Delta_{{MA}\; X} = \frac{\begin{matrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}}} & \lbrack 22\rbrack \end{matrix}$ Yielding: Combining terms found in [15], [21] and [24] into [3] results in:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right)}{{abs}\left( \frac{\begin{matrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)}}}} & \lbrack 23\rbrack \end{matrix}$ With numerator and denominator simplification in [23]:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{{abs}\begin{pmatrix} {m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right) \end{pmatrix}}{{abs}\begin{pmatrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}}}}} & \lbrack 24\rbrack \end{matrix}$

Finally, FIG. 8 depict a further scenario wherein the slope of the Average RS SNR_([ACTUAL]) 156 is greater than the slope of the RS SNR_([IDEAL]) 78 and actual RS SNR is above Average RS SNR. Thus, FIG. 8 depicts wherein y₂ (151) and y₃ (152), match with the RSRP variables x₁ (153), x₂ (154), and x₃ (155) to define the delta min 157 and delta max 156, and the area of interest 159. This FIG. 8 is represented by the formulae as:

Formula:

The formula for Priority (%) is defined as: Priority (%)=Priority_(BASE)+Priority_(INCREMENT)  [1] Where:

$\begin{matrix} {{Priority}_{BASE} = {{Any}\mspace{11mu}\left( {{10\%},\;{20\%},\;{30\%},\;{40\%},\;{50\%},\;{60\%},\;{70\%},\;{80\%},\;{90\%}} \right)}} & \lbrack 2\rbrack \\ {\mspace{79mu}{{Priority}_{INCREMENT} = {0.1 \times \frac{{{abs}(\Delta)} - {{abs}\left( \Delta_{MIN} \right)}}{{{abs}\left( \Delta_{MAX} \right)} - {{abs}\left( \Delta_{MIN} \right)}}\mspace{14mu}{such}\mspace{14mu}{that}\text{:}}}} & \lbrack 3\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{MIN}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {0\%}}}} & \lbrack 4\rbrack \\ {\mspace{79mu}{{{when}\mspace{14mu}\Delta} = {{\Delta_{MAX}\mspace{14mu}{then}\mspace{14mu}{Priority}_{INCREMENT}} = {10\%}}}} & \lbrack 5\rbrack \\ {\mspace{79mu}{\Delta = {{{RS}\mspace{11mu}{SNR}_{ACTUAL}} - {{RS}\mspace{11mu}{SNR}_{IDEAL}}}}} & \lbrack 6\rbrack \end{matrix}$ Let: a) Average RS SNR_(ACTUAL) be directly proportional to RS SNR_(ACTUAL) as a linear function y=m*x+a and defined as: Average RS SNR_(ACTUAL) =m _(AVERAGE RS SNR)×RSRP_(ACTUAL) +a _(AVERAGE RS SNR)  [7] b) RS SNR_(IDEAL) be equal to RSRP_(ACTUAL) offset by 125.2 defined as: RS SNR_(IDEAL)=RSRP_(ACTUAL)+125.2  [8] c) Average RS SNR_(ACTUAL) have a slope smaller than RS SNR_(IDEAL) defined as: m _(AVERAGE RS SNR) _(ACTUAL) >m _(RS SNR) _(IDEAL)   [9] d) RS SNR_(ACTUAL) be between RS SNR_(Threshold1) (point y₂ on y-axis) and RS SNR_(Threshold2) (point y₃ on y-axis) defined as: RS SNR(y ₂)=RS SNR_(Threshold1)≤RS SNR_(ACTUAL)  [10] and RS SNR_(ACTUAL)<RS SNR(y ₃)=RS SNR_(Threshold2)  [11] e) RSRP_(ACTUAL) be between RSRP_(Threshold1) (point x₁ on x-axis) and RSRP_(Threshold2) (point x₃ on x-axis) defined as: RSRP(x ₁)=RSRP_(Threshold1)≤RSSP_(ACTUAL)  [12] and RSRP_(ACTUAL)<RSRP(x ₃)=RSRP_(Threshold2)  [13] Then: The difference between RS SNR_(ACTUAL) and RS SNR_(IDEAL) is defined as Δ: Δ=RS SNR_(ACTUAL)−RS SNR_(IDEAL)=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [14] In combination with [8], Δ can be simplified as: Δ=RS SNR_(ACTUAL)−RSRP_(ACTUAL)−125.2  [15] The minimum distance (Δ_(MIN)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is along the line RS SNR_(IDEAL) thus equal to zero: Δ_(MIN)=0  [16] The maximum distance (Δ_(MAX)) from RS SNR_(ACTUAL) to RS SNR_(IDEAL) is at point (x₃, y₂) on line Average RS SNR_(ACTUAL), defined as: Δ_(MAX)=Δ(x ₃ ,y ₂)=Average RS SNR_(ACTUAL)(x ₃)−RS SNR_(IDEAL)(x ₃)  [17] Using the definition in [7]:

$\begin{matrix} {{{Average}\mspace{14mu}{RS}\mspace{11mu}{{SNR}_{Actual}\left( x_{3} \right)}} = {{{m_{{Avearge}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}} \times {{RSRP}\left( x_{3} \right)}} + a_{{Average}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}} = {{RS}\mspace{11mu}{{SNR}\left( y_{2} \right)}}}} & \lbrack 18\rbrack \\ {\mspace{79mu}{{{RSRP}\left( x_{3} \right)} = \frac{{{RS}\mspace{11mu}{SNR}\mspace{11mu}\left( y_{2} \right)} - a_{{Average}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}}}} & \lbrack 19\rbrack \end{matrix}$ Using the definition in [8]:

$\begin{matrix} {\mspace{79mu}{{{RS}\mspace{11mu}{{SNR}_{IDEAL}\left( x_{3} \right)}} = {{{RSRP}\left( x_{3} \right)} + 125.2}}\mspace{14mu}} & \lbrack 20\rbrack \\ {{{Combining}\mspace{14mu}{terms}\mspace{14mu}{found}\mspace{14mu}{{in}\mspace{14mu}\lbrack 18\rbrack}},{{\lbrack 19\rbrack\mspace{14mu}{{and}\mspace{14mu}\lbrack 20\rbrack}\mspace{14mu}{{into}\mspace{14mu}\lbrack 17\rbrack}\mspace{14mu}{results}\mspace{14mu}{in}\text{:}\mspace{11mu}\Delta_{MAX}} = {{{RS}\mspace{14mu}{{SNR}\left( y_{2} \right)}} - \frac{{{RS}\mspace{11mu}{{SNR}\left( y_{2} \right)}} - a_{{Average}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}}{m_{{Avearge}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}} - 125.2}}} & \lbrack 21\rbrack \end{matrix}$ Using the limit defined in [10] and re-arranging [21] results in:

$\begin{matrix} {\Delta_{MAX} = \frac{\begin{matrix} {{m_{{Avearge}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{11mu}{SNR}_{{Threshold}\; 1}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{11mu}{SNR}_{{Threshold}\; 1}} - a_{{Average}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{11mu}{RS}\mspace{11mu}{SNR}_{ACTUAL}}}} & \lbrack 22\rbrack \end{matrix}$ Yielding: Combining terms found in [15], [21] and [24] into [3] results in:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{{abs}\left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right)}{{abs}\left( \frac{\begin{matrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{matrix}}{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right)}}}} & \lbrack 23\rbrack \end{matrix}$ With numerator and denominator simplification in [23]:

$\begin{matrix} {{{Priority}(\%)} = {{Priority}_{BASE} + {0.1 \times \frac{{abs}\begin{pmatrix} {m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times} \\ \left( {{{RS}\mspace{14mu}{SNR}_{ACTUAL}} - {RSRP}_{ACTUAL} - 125.2} \right) \end{pmatrix}}{{abs}\begin{pmatrix} {{m_{{Avearge}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}} \times \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - 125.2} \right)} -} \\ \left( {{{RS}\mspace{14mu}{SNR}_{{Threshold}\; 2}} - a_{{Average}\mspace{14mu}{RS}\mspace{14mu}{SNR}_{ACTUAL}}} \right) \end{pmatrix}}}}} & \lbrack 24\rbrack \end{matrix}$

Accordingly, based upon a data point we can calculate the optimization priority for all data points. This allows providers to identify those networks that can be optimized with the greatest efficiency to improve service. This gives such providers a huge improvement in the manner in which they optimize any given data point.

Accordingly, a scenario to identify the use cases for the above formulae and the corresponding graphical depictions is wherein a data set of 1000 measurements exists, with each measurement having an RSRP and a corresponding RS SNR. The group of 1000 measurements are plotted on an RSRP over RS SNR plot and a generates a line that best fits the scatter plot. This line is the Average RS SNR as a function of RSRP, as depicted in the plot figures. We then compare the slope of this line to the line defined by the Idea Rs SNR, which is also a function of RSRP. Using y₂, y₃ and x₂, x₃ limits we find the location of delta_min and delta_max. The shaded area as depicted in FIGS. 5-8 is the location of the some of the 1000 measurements that fall within the y and x limits. We calculate Optimization Priority for points that are within the shaded area.

Accordingly, when capturing a set of measurements, we can utilize the formulae defined herein to generate graphical plots that define an optimization priority for the datapoint within the data set. This allows an entity to evaluate a data set and to define how to priority optimization of the data sets.

This optimization priority can be further combined by the ICL to identify both internal and external considerations with regard to the coverage and quality, and also to the priority of which the optimization should occur. Thus, the methods and systems herein can be utilized alone or in combination with one another to improve network service.

The inventions and embodiments thereof now being fully described as would be understood by a person of ordinary skill in the art, are set forth such that slight modifications may be made without deviating from the inventive nature of the embodiments. The following appended claims now set forth the scope of one or more of the embodiments as described herein. 

What is claimed is:
 1. A method of creating an indoor confidence level comprising: receiving measurements comprising a location and location accuracy value from or for a device, wherein the location accuracy value is equated to a location accuracy circle; comparing the location and location accuracy circle to a map of known buildings and outdoor locations; wherein the map of known buildings is an electronically defined map comprising a plurality of polygons, each polygon defining a known building or structure, where within a perimeter of each polygon is defined as being indoors, and wherein all other space on said map is defined as outdoors; and defining an initial indoor confidence level based upon the percent of overlap of the accuracy radius to a building on said map; wherein the initial indoor confidence level is modified by (1) detecting whether the device is connected to a WiFi network; (2) detecting whether a battery in the device is charging; (3) detecting if the device is stationary; (4) detecting whether a device moves a distance in a period of time “T”, wherein said distance provides an indication of unreliable location mapping; and (5) detecting a location and comparing said location to a similar time and location from a previous day; and updating the initial indoor confidence.
 2. The method of claim 1, wherein the process of detecting whether a device is connected to a WiFi network is further defined by comparing the strength of the WiFi signal to a signal threshold variable in order to determine whether a device is connected to a nearby indoor WiFi network.
 3. The method of claim 1, wherein the process of detecting whether the device battery is charging detects whether the device is connected to external power.
 4. The method of claim 1, wherein the process of detecting if the device is stationary at a high confidence location comprises detecting if a device is stationary for a time period “T” and wherein the high confidence location is one that has an indoor confidence level of at least 50, or which has previously been categorized as being indoors.
 5. The method of claim 1, wherein the process of detecting whether the device moves a significant distance in a period of time “T” is defined as capturing a set of location datapoints and comparing a set of location datapoints over a time period T₁, and wherein if any data point moves a distance greater than possible during the period of time “T” then that distance is identified as being unreliable.
 6. The method of claim 1, wherein the distance being unreliable provides an increase in the indoor confidence level.
 7. The method of claim 1, wherein the method captures data at a first time and stores a location and a time in a database, and wherein a data point, captured at a different day, at the same time of day, is compared to the location of the first time, and wherein if the location is the about the same, then the indoor confidence level is increased.
 8. The method of claim 1, wherein the indoor confidence level is modified due to any of the additional steps, the indoor confidence level is a medium confidence level.
 9. The method of claim 8, wherein the medium confidence level being defined, and wherein the location accuracy radius overlaps with any building, the location is reported as in that building.
 10. The method of claim 8, wherein the medium confidence level being defined, and wherein the location accuracy radius does not overlap with a building, the location is reported as not within in that building. 